Building Trust in Earth Science Findings through Data Traceability and Results Explainability
| Author(s) | Olaya, Paula | |
| Author(s) | Kennedy, Dominic | |
| Author(s) | Llamas, Ricardo | |
| Author(s) | Valera, Leobardo | |
| Author(s) | Vargas, Rodrigo | |
| Author(s) | Lofstead, Jay | |
| Author(s) | Taufer, Michela | |
| Date Accessioned | 2023-01-06T19:54:21Z | |
| Date Available | 2023-01-06T19:54:21Z | |
| Publication Date | 2022-11-08 | |
| Description | This article was originally published in IEEE Transactions on Parallel and Distributed Systems. The version of record is available at: https://doi.org/10.1109/TPDS.2022.3220539 | |
| Abstract | To trust findings in computational science, scientists need workflows that trace the data provenance and support results explainability. As workflows become more complex, tracing data provenance and explaining results become harder to achieve. In this paper, we propose a computational environment that automatically creates a workflow execution's record trail and invisibly attaches it to the workflow's output, enabling data traceability and results explainability. Our solution transforms existing container technology, includes tools for automatically annotating provenance metadata, and allows effective movement of data and metadata across the workflow execution. We demonstrate the capabilities of our environment with the study of SOMOSPIE, an earth science workflow. Through a suite of machine learning modeling techniques, this workflow predicts soil moisture values from the 27 km resolution satellite data down to higher resolutions necessary for policy making and precision agriculture. By running the workflow in our environment, we can identify the causes of different accuracy measurements for predicted soil moisture values in different resolutions of the input data and link different results to different machine learning methods used during the soil moisture downscaling, all without requiring scientists to know aspects of workflow design and implementation. | |
| Sponsor | This work supported in part by Sandia National Laboratories, in part by National Science Foundation through under Grants 1841758, 1941443, 2028923, 2103845, 2103836, and 2138811, in part by IBM through a Shared University Research Award, and in part by XSEDE program through the NSF under Grant 1548562. | |
| Citation | P. Olaya et al., "Building Trust in Earth Science Findings through Data Traceability and Results Explainability," in IEEE Transactions on Parallel and Distributed Systems, vol. 34, no. 2, pp. 704-717, 1 Feb. 2023, doi: 10.1109/TPDS.2022.3220539. | |
| ISSN | 1558-2183 | |
| URL | https://udspace.udel.edu/handle/19716/32017 | |
| Language | en_US | |
| Publisher | IEEE Transactions on Parallel and Distributed Systems | |
| Keywords | scientific workflows | |
| Keywords | scientific computing | |
| Keywords | provenance | |
| Keywords | reproducibility | |
| Keywords | replicability | |
| Keywords | soil moisture predictions | |
| Title | Building Trust in Earth Science Findings through Data Traceability and Results Explainability | |
| Type | Article |
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